Artificial Intelligence and Machine Learning for Cardiovascular Computed Tomography (CCT): A White Paper of the Society of Cardiovascular Computed Tomography (SCCT)
Artificial intelligence (AI), and machine learning (ML) in particular, are rapidly transforming the world around us. In healthcare, AI/ML has the potential to improve every point in the clinical pathway. For cardiovascular computed tomography (CT) AI/ML could aid patient selection and screening, referrals and scheduling, image acquisition and reconstruction, image analysis and diagnosis, report generation, prognostication and risk stratification, management recommendations and follow-up. However, there are important challenges that must be considered for the development, assessment, and implementation of AI/ML to ensure that it is safe, reliable, cost effective, and improves outcomes for patients. In this white paper from the Society of Cardiovascular Computed Tomography we discuss current state-of-the-art applications of AI/ML in cardiovascular CT, highlight challenges and considerations for both research and implementation in clinical practice, and explore the future of this technology. This white paper reflects the expert opinions of the writing group and SCCT guidelines were followed for the selection of the writing group and development of the white paper.
Authors
- Márton Kolossváry (ORCID: https://orcid.org/0000-0002-5570-991X)
- Lauren A. Baldassarre (ORCID: https://orcid.org/0000-0003-4173-3449)
- Andrew D. Choi (ORCID: https://orcid.org/0000-0003-1500-0428)
- Leslee J. Shaw (ORCID: https://orcid.org/0000-0003-1268-1491)
- Michelle Claire Williams (ORCID: https://orcid.org/0000-0003-3556-2428)
- Marly van Assen (ORCID: https://orcid.org/0000-0003-4044-4426)
- Damini Dey (ORCID: https://orcid.org/0000-0003-2236-6970)
- Koen Nieman (ORCID: https://orcid.org/0000-0002-8312-0598)
- Andrew Lin (ORCID: https://orcid.org/0000-0003-0348-7697)
- Jonathan Richard Weir-McCall (ORCID: https://orcid.org/0000-0001-5842-842X)
- Edward David Nicol (ORCID: https://orcid.org/0000-0001-9280-787X)
- Michael T. Lu (ORCID: https://orcid.org/0000-0003-4696-9610)
- Marc R. Dweck (ORCID: https://orcid.org/0000-0001-9847-5917)
- Manish Motwani (ORCID: https://orcid.org/0000-0003-4483-5168)
- Carlo N. De Cecco (ORCID: https://orcid.org/0000-0002-2956-3101)
- Ivana Išgum (ORCID: https://orcid.org/0000-0003-1869-5034)
- Jonathon Leipsic (ORCID: https://orcid.org/0009-0002-3892-9029)
Institutions
- Obuda University (HU)
- Cedars-Sinai Medical Center (US)
- University of British Columbia (CA)
- George Washington University (US)
- Guy's and St Thomas' NHS Foundation Trust (GB)
- Royal Papworth Hospital (GB)
- Emory University Hospital (US)
- Yale University (US)
- Gordon Center for Medical Imaging (US)
- Massachusetts General Hospital (US)
- Manchester University NHS Foundation Trust (GB)
- Monash Health (AU)
- Australian Centre for Heart Health (AU)
- Amsterdam University Medical Centers (NL)
- University of Amsterdam (NL)
- Stanford University (US)
- University of Edinburgh (GB)
- Icahn School of Medicine at Mount Sinai (US)
Publication Details
- Journal
- Journal of cardiovascular computed tomography
- Published
- 2024-08-30
- DOI
- https://doi.org/10.1016/j.jcct.2024.08.003
- Citations
- 27
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.98